Chen Liu

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Chen Liu is an author.


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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Crew: cross-modal resource searching by exploiting Wikipedia Multi-modal
Web 2.0
English 2010 In Web 2.0, users have generated and shared massive amounts of resources in various media formats, such as news, blogs, audios, photos and videos. The abundance and diversity of the resources call for better integration to improve the accessibility. A straightforward approach is to link the resources via tags so that resources from different modals sharing the same tag can be connected as a graph structure. This naturally motivates a new kind of information retrieval system, named cross-modal resource search, in which given a query object from any modal, all the related resources from other modals can be retrieved in a convenient manner. However, due to the tag homonym and synonym, such an approach returns results of low quality because resources with the same tag but not semantically related will be directly connected as well. In this paper, we propose to build the resource graph and perform query processing by exploiting Wikipedia. We construct a concept middle-ware between the layer of tags and resources to fully capture the semantic meaning of the resources. Such a cross-modal search system based on Wikipedia, named Crew, is built and demonstrates promising search results. 0 0
Integrating web 2.0 resources by Wikipedia English 2010 The concept of Web 2.0 becomes prevalent and popular in the past few years. People are able to share and manage their own resources in Web 2.0 Systems. The abundance of Web 2.0 resources in various media formats calls for better resource integration, intending to enrich user experience in both browsing and searching. Though the Web 2.0 resources are shown in various modalities, their tags act as an intuitive medium to connect resources together. However, tagging is by nature an ad hoc activity. They do often contain noises and are affected by the subjective inclination of taggers. Consequently, linking resources simply by tags will not be reliable. In this paper, we propose an effective approach for linking tagged resources to concepts extracted from Wikipedia, which has become a fairly reliable reference over the last few years. Compared to the tags, the concepts are therefore of higher quality. Empirical experiments were conducted, and the results validate the effectiveness of our framework. 0 0